Our model is composed of blocks derived from Inception that integrates batch normalization (BN) [1]} after each convolution step. For each Inception module, the feature number per branch is determined during validation. Due to the concatenation that takes place in each module, the feature output number is four times greater than the feature number per branch value. In the branches that contain the filters with 5x5 and 3x3 kernels, dimensionality reduction is applied to minimize memory consumption through filters with 1x1 kernels. The number of dimensionality reduction filters is set by the (\(\alpha \) ) and (\(\beta \) ) parameters, which vary depending on the feature number per branch (see Figure REF ).
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